class_docstr: Randomly drop some regions of feature maps.
  
   Please refer to the method proposed in `DropBlock
   <https://arxiv.org/abs/1810.12890>`_ for details.
  
  Args:
      drop_prob (float): The probability of dropping each block.
      block_size (int): The size of dropped blocks.
      warmup_iters (int): The drop probability will linearly increase
          from `0` to `drop_prob` during the first `warmup_iters` iterations.
          Default: 2000.
docstr: Compute the value of gamma according to paper. gamma is the
  parameter of bernoulli distribution, which controls the number of
  features to drop.
  
  gamma = (drop_prob * fm_area) / (drop_area * keep_area)
  
  Args:
      feat_size (tuple[int, int]): The height and width of feature map.
  
  Returns:
      float: The value of gamma.
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code: def _compute_gamma(self, feat_size):
      """Compute the value of gamma according to paper. gamma is the
      parameter of bernoulli distribution, which controls the number of
      features to drop.
  
      gamma = (drop_prob * fm_area) / (drop_area * keep_area)
  
      Args:
          feat_size (tuple[int, int]): The height and width of feature map.
  
      Returns:
          float: The value of gamma.
      """
      gamma = self.drop_prob * feat_size[0] * feat_size[1]
      gamma /= (feat_size[0] - self.block_size + 1) * (
          feat_size[1] - self.block_size + 1
      )
      gamma /= self.block_size**2
      factor = (
          1.0
          if self.iter_cnt > self.warmup_iters
          else self.iter_cnt / self.warmup_iters
      )
      return gamma * factor
  
file_path: mmdetection/mmdet/models/layers/dropblock.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the DropBlock obj's underlying member method code with example usage: obj._compute_gamma(feat_size) and returns: unknown